Active Semi-Supervision for Pairwise Constrained Clustering (2004)
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannot-link constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision.
In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.

Sugato Basu Ph.D. Alumni sugato [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu